signal_design.default_functions

This is where default function are defined.

Module Contents

Functions

get_using_end(→ Axis)

Create instance of Axis using start, end and sample params of axis.

get_size(→ int)

Calculate size of axis using start, end and sample.

get_array(→ numpy.ndarray)

Create array of numpy from Axis, using, start, end and size of Axis.

get_axis_from_array(→ Axis)

Create instance of Axis from some array of numbers.

get_actual_sample(→ signal_design.help_types.RealNumber)

Calculate actual sample. Use for self-test. Problem with floating point

get_common_axis(→ Axis)

Specifies the overall axis.

math_operation(→ signal_design.help_types.Y)

Math operations.

one_integrate(→ float)

Integration.

integrate(→ RelationT)

Integration.

differentiate(→ RelationT)

Differentiation.

interpolate_extrapolate(...)

Interpolation and extrapolation

correlate(→ RelationT)

Correlation.

convolve(→ RelationT)

Convolution.

signal_shift(→ signal_shift.signal)

Shifting of signal.

signal2spectrum(→ signal_design.spectrum.Spectrum)

Forward Fourier Transform.

_calculate_spectrum(→ signal_design.spectrum.Spectrum)

get_frequency_axis(→ Frequency)

Get frequency axis from size and sample of time.

spectrum2signal(→ signal_design.signal.Signal)

Inverse Fourier Transform.

get_time_axis(→ Time)

Get time axis from frequency axis and desired size of time axis.

integrate_function(→ RelationT)

Integration function y(x).

Attributes

RelationT

integration

Axis

Axis of x.
signal_design.default_functions.RelationT
signal_design.default_functions.integration
signal_design.default_functions.Axis

Axis of x.

signal_design.default_functions.get_using_end(start: signal_design.help_types.RealNumber, end: signal_design.help_types.RealNumber, sample: signal_design.help_types.RealNumber, is_correct_end: bool = True) → Axis[source]

Create instance of Axis using start, end and sample params of axis.

Parameters:

  • start (RealNumber) – start position of axis.

  • end (RealNumber) – end position of axis.

  • sample (RealNumber) – sample of axis.

  • is_correct_end (bool) – Default True. Checking the correctness of the end of the axis. If value is False, no end will be set for the instance.

Returns:

new instance of Axis.

Return type:

Axis

signal_design.default_functions.get_size(start: signal_design.help_types.RealNumber, end: signal_design.help_types.RealNumber, sample: signal_design.help_types.RealNumber) → int[source]

Calculate size of axis using start, end and sample.

Parameters:

  • start (RealNumber) – start position of axis.

  • end (RealNumber) – end position of axis.

  • sample (RealNumber) – sample of axis.

Returns:

size of axis.

Return type:

int

signal_design.default_functions.get_array(axis_data: signal_design.axis.Axis) → numpy.ndarray[source]

Create array of numpy from Axis, using, start, end and size of Axis.

Parameters:

axis_data (Axis) – instance of Axis

Returns:

numpy array of axis.

Return type:

numpy.ndarray

signal_design.default_functions.get_axis_from_array(x: signal_design.help_types.ArrayLike, sample: Optional[signal_design.help_types.RealNumber] = None) → Axis[source]

Create instance of Axis from some array of numbers.

Parameters:

  • x (ArrayLike) – Input array like of numbers.

  • sample (Optional[RealNumber]) – Default None. Sample of array. If sample is None, it well be calculated by function.

Returns:

new instance of Axis.

Return type:

Axis

signal_design.default_functions.get_actual_sample(x: numpy.ndarray) → signal_design.help_types.RealNumber[source]

Calculate actual sample. Use for self-test. Problem with floating point in Python (https://docs.python.org/3/tutorial/floatingpoint.html).

Parameters:

x (numpy.ndarray) – array of numbers.

Returns:

an actual sample

Return type:

RealNumber

signal_design.default_functions.get_common_axis(axis1: Axis, axis2: Axis, is_correct_end: bool = False) → Axis[source]

Specifies the overall axis.

Finds the general sample rate and beginning and end of sequence. A function by which to find the common sequence of numbers along the axis, obtained from two other sequences along the axis.

Parameters:

  • axis1 (Axis) – first axis.

  • axis2 (Axis) – second axis.

Returns:

return common Axis for first and second axes.

Return type:

Axis

signal_design.default_functions.math_operation(y1: numpy.ndarray, y2: Union[numpy.ndarray, signal_design.help_types.Number], name_operation: signal_design.core.MathOperation) → signal_design.help_types.Y[source]

Math operations.

Using numpy math operations.

Parameters:

  • y1 (numpy.ndarray) – first sequence y.

  • y2 (Union[numpyp.ndarray, Number]) – second sequence y or other number

  • name_operation (MathOperation) – which mathematical operation (+, -, *, / and etc.)

Raises:

TypeFuncError – if operation can not be executed.

Returns:

result of math operation.

Return type:

Y

signal_design.default_functions.one_integrate(relation: RelationT) → float[source]

Integration.

Taking the integral on a segment. Return of the area under the graph. using scipy trapezoid integration.

Parameters:

relation (Relation) – from will be calculated integral.

Returns:

result of integration.

Return type:

float

signal_design.default_functions.integrate(relation_data: RelationT) → RelationT[source]

Integration.

Integration across the entire function. Get the expected integrated array function. Using the scipy.integrate.cumtrapz function.

Parameters:

relation (Relation) – integrated function.

Returns:

result of integration of function.

Return type:

Relation

signal_design.default_functions.differentiate(relation_data: RelationT) → RelationT[source]

Differentiation.

The function by which differentiation is performed. Using the numpy.diff function.

Parameters:

relation (Relation) – function which will be differentiated.

Returns:

result of differentiation.

Return type:

Relation

signal_design.default_functions.interpolate_extrapolate(x: signal_design.help_types.X, y: signal_design.help_types.Y, bounds_error=False, fill_value=0.0) → Callable[[signal_design.axis.Axis], signal_design.help_types.Y][source]

Interpolation and extrapolation

Using the scipy.interpolate.interp1d function. Returning function of interpolation.

Parameters:

  • x (numpy.ndarray) – numbers array of axis. Samples can be not equal.

  • y (numpy.ndarray) – representation interpolated extrapolated functions as array.

  • bounds_error (bool, optional) – if False then do not raise error if new array behind of bound old array. Defaults to False.

  • fill_value (float, optional) – default fill value if other not expected. Defaults to 0.0.

Returns:

Callable that get first new array of x and return

interpolate-extrapolate result.

Return type:

Callable[[X], Y]

signal_design.default_functions.correlate(cls: Type[RelationT], r1: RelationT, r2: RelationT) → RelationT[source]

Correlation.

The function by which the correlation is performed. Using the numpy.correlate function.

Parameters:

  • cls (Type[Relation]) – class to use equalization of two relations.

  • r1 (Relation) – first function y.

  • r2 (Relation) – second function y.

Returns:

result of correlation.

Return type:

Relation

signal_design.default_functions.convolve(cls: Type[RelationT], r1: RelationT, r2: RelationT) → RelationT[source]

Convolution.

The function by which the convolution is performed. Using the numpy.convolve function.

Parameters:

  • cls (Type[Relation]) – class to use equalization of two arrays.

  • r1 (Relation) – first function y.

  • r2 (Relation) – second function y.

Returns:

result of convolution.

Return type:

Relation

signal_design.default_functions.signal_shift(signal: signal_shift.signal, x_shift: signal_design.help_types.RealNumber = 0) → signal_shift.signal[source]

Shifting of signal.

Shift signal using Fourier transform.

Parameters:

  • signal – input shifting signal.

  • x_shift – shift distance.

Returns:

shifted signal.

Return type:

Signal

signal_design.default_functions.signal2spectrum(relation_data: RelationT, frequency: Optional[Union[Frequency, int]] = None, is_start_zero=False, is_real_value_transform=True) → signal_design.spectrum.Spectrum[source]

Forward Fourier Transform.

Function for converting a signal into a spectrum. Using the numpy.fft.rfft function.

Parameters:

  • relation_data (Relation) – signal from which get spectrum.

  • frequency (Axis, int, optional) – Define frequency to calculate spectrum. Defaults to None.

  • is_start_zero (bool, optional) – Consider array started from zero time. Defaults to False.

Returns:

result transformation Signal to Spectrum.

Return type:

Relation

signal_design.default_functions._calculate_spectrum(time: Time, amplitude: numpy.ndarray, frequency: Optional[Union[int, Frequency]] = None, is_real_value_transform: bool = True) → signal_design.spectrum.Spectrum[source]
signal_design.default_functions.get_frequency_axis(time_size: int, time_sample: float, is_complex_data: bool = False) → Frequency[source]

Get frequency axis from size and sample of time.

Function for getting a frequency axis using size and sample of time. If the data is complex, then the frequency axis will contain negative part.

Parameters:

  • time_size (int) – size of time

  • time_sample (float) – sample of time

  • is_complex_data (bool) – If the data is complex, then the frequency axis will contain a negative part. If not, then vice versa. Defaults to False.

Returns:

frequency axis.

Return type:

Axis

signal_design.default_functions.spectrum2signal(relation_data: signal_design.relation.Relation, time: Optional[Union[Time, int]] = None, time_start: Union[float, None] = None) → signal_design.signal.Signal[source]

Inverse Fourier Transform.

Function for converting a spectrum into a signal. Using numpy.ifft function.

Parameters:

  • relation (Relation) – spectrum of signal.

  • time (Axis, int, optional) – Define time to calculate signal. Defaults to None.

  • time_start (float, optional) – default fft convert to 0. time. Maybe you want another start of time. Defaults to None.

Returns:

result transformation Spectrum to Signal.

Return type:

Signal

signal_design.default_functions.get_time_axis(frequency: Frequency, size: Optional[int] = None, time_start: Optional[float] = None) → Time[source]

Get time axis from frequency axis and desired size of time axis.

Function for getting a time axis using frequency axis and desired size.

Parameters:

  • frequency (Axis) – frequency axis.

  • size (int, optional) – desired size of time axis. Defaults to None.

  • time_start (float, optional) – default fft convert to 0. time. Maybe you want another start of time. Defaults to None.

Returns:

time axis.

Return type:

Axis

signal_design.default_functions.integrate_function(function: Callable[[signal_design.help_types.X], signal_design.help_types.Y], x: Axis) → RelationT[source]

Integration function y(x).

The function by which the integration function is performed. Integration across the entire function. Get the expected integrated array function. Integration of function, using scipy.integrate.quad function.

Parameters:

  • function (Callable[[X], Y]) – function is describing changes frequency from time.

  • x (Axis) – x axis.

Returns:

result of integration function.

Return type:

Relation